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Hands-On Neural Networks

You're reading from  Hands-On Neural Networks

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781788992596
Pages 280 pages
Edition 1st Edition
Languages
Authors (2):
Leonardo De Marchi Leonardo De Marchi
Profile icon Leonardo De Marchi
Laura Mitchell Laura Mitchell
Profile icon Laura Mitchell
View More author details
Toc

Table of Contents (16) Chapters close

Preface 1. Section 1: Getting Started
2. Getting Started with Supervised Learning 3. Neural Network Fundamentals 4. Section 2: Deep Learning Applications
5. Convolutional Neural Networks for Image Processing 6. Exploiting Text Embedding 7. Working with RNNs 8. Reusing Neural Networks with Transfer Learning 9. Section 3: Advanced Applications
10. Working with Generative Algorithms 11. Implementing Autoencoders 12. Deep Belief Networks 13. Reinforcement Learning 14. Whats Next? 15. Other Books You May Enjoy

Understanding word embeddings

Embedding is a mathematical structure contained within another instance. If we are embedding an object X in an object Y, we will be preserving the structure of the objects and so the instance.

Word embedding is a technique to map words to vectors, creating a multidimensional space that will allow the creation of similar representations for similar words. Each word is represented by a single vector with often tens or hundreds of dimensions, in contrast to other representations such as one-hot encoding that can have thousands or even millions of dimensions.

When we have words in the form of vectors, we end up using all mathematical techniques that we would on pure numbers. Also when transformed into vectors, words will keep the same proprieties that numbers have.

It also means that we could start doing operations as follows:

King - man + woman =queen...

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